Σχόλια 0

Το κείμενο του εγγράφου

For The Cambridge Handbook to Artificial Intelligence

History, motivations and core themes of AI

By Stan Franklin

Introduction

This chapter is aimed at introducing the reader to field of artificial intelligence(AI) in the context of its history and core themes. After a concise preambleintroducing these themes, a brief and highly selective history will be presented.This history will be followed by asuccinct

introduction to the major researchareas within AI. The chapter will continue with a description of currents trends inAI research, and will conclude with a discussion of the current situation withregard to the core themes. The current trends are best understood in terms of AIhistory, its core themes and its traditional research areas. My goal is

to providethe reader with sufficient background context for understanding and appreciatingthe subsequent chapters in this volume.

Overview of Artificial Intelligence core themes

The history of artificial intelligence may be best understood in the context ofits core themes and controversies. Below is a brief listing of such AI distinctions,issues, themes and controversies. It would be well to keep these in mind duringyour reading of the rest of this chapter. Each of the themes will be expandedupon and clarified as the chapter progresses. Many of these result from theirbeing, to this day, no agreed up definition of intelligence within the AI communityof researchers.

Smart Software vs. Cognitive Modeling

AI has always been a part of computer science, an engineering disciplineaimed at creating smart computer programs, that is, intelligent software productsto meet human needs. We’ll see a number of examples of such smart software.AI also has its science side that’s aimed at helping us understand humanintelligence. This endeavor includes building software systems that “think” inhuman like ways, as well as producing computational models of aspects ofhuman cognition. Such computational models provide hypotheses to cognitivescientists.

Symbolic AI vs. Neural Nets

From its very inception artificial intelligence was divided into two quite distinctresearch streams, symbolic AI and neural nets. Symbolic AI took the view thatintelligence could be achieved by manipulating symbols within the computeraccording to rules. Neural nets, or connectionism as the cognitive scientistscalled it, instead attempted to create intelligent systems as networks of nodeseach comprising a simplified model of a neuron. Basically, the difference wasbetween a computer analogy and a brain analogy, between implementing AIsystems as traditional computer programs and modeling them after nervoussystems.

Reasoning vs. Perception

Here the distinction is between intelligence as high-level reasoning fordecision-making, say in machine chess or medical diagnosis, and the lower-levelperceptual processing involved in, say machine vision, the understanding ofimages by identifying objects and their relationships.

Reasoning vs. Knowledge

Early symbolic AI researchers concentrated on understanding themechanisms (algorithms) used for reasoning in the service of decision-making.The assumption was that understanding how such reasoning could beaccomplished in a computer would be sufficient to build useful smart software.Later, they realized that, in order to scale up for real-world problems, they had tobuild significant amounts of knowledge into their systems. A medical diagnosissystem had to know much about medicine, as well as being able to drawconclusions.

To Represent or Not

Such knowledge had to be represented somehow within the system, that is,the system had to somehow model its world. Such representation could takevarious forms, including rules. Later, a controversy arose as to how much of suchmodeling actually needed to be done. Some claimed that much could beaccomplished without such internal modeling.

Brain in a Vat vs. Embodied AI

The early AI systems had humans entering input into the systems and actingon the output of the systems. Like a “brain in a vat” these systems could neithersense the world nor act on it. Later, AI researchers created embodied, orsituated) AI systems that directly sensed their worlds and also acted on themdirectly. Real world robots are examples of embodied AI systems.

Narrow AI vs. Human Level Intelligence

In the early days of AI many researchers aimed at creating human-levelintelligence in their machines, the so-called “strong AI.” Later, as theextraordinary difficulty of such an endeavor became more evident, almost all AIresearchers built systems that operated intelligently within some relatively narrowdomain such as chess or medicine. Only recently has there been a move back inthe direction of systems capable of a more general, human-level intelligence thatcould be applied broadly acrossdiverse domains.

Some Key Moments in AI

McCulloch and Pitts

The neural nets branch of AI began with a very early paper by WarrenMcCulloch and Walter Pitts (1943). McCulloch, a professor at the University ofChicago, and Pitts, then an undergraduate student, developed a much-simplifiedmodel of a functioning neuron, a McCulloch-Pitts unit. They showed thatnetworks of such units could perform any Boolean operation (and, or, not) and,thus, any possible computation. Each of these units compared the weighted

Alan Turing, a Cambridge mathematician of the first half of the twentiethcentury, can be considered the father of computing (its grandfather was CharlesBabbage during the mid-nineteenth century) and the grandfather of artificialintelligence. During the Second World War in 1939-1994 Turingpitted his witsagainst the Enigma cipher machine, the key to German communications. He ledin developing the British Bombe, an early computing machine that was used overand over to decode messages encoded using the Enigma.

During the early twentieth century Turing and others were interested inquestions of computability. They wanted to formalize an answer to the questionof which problems can be solved computationally. Several people developeddistinct such formalisms. Turing offered the Turing Machine (1936), AlonzoChurch the Lambda Calculus (1936), and Emil Post the

Production System(1943). These three apparently quite different formal systems soon proved to belogically equivalent in defining computability, that is, for specifying thoseproblems that can be solved by a program running on a computer. The Turingmachine proved to be the most useful formalization, and is the one most oftenused in theoretical computer science.

In 1950

Turing published the very first paper suggesting the possibility ofartificial intelligence (1950). In it he first described what we now

call the Turingtest, and offered it as a sufficient condition for the existence of AI. The Turing testhas human testers conversing in natural language

without constraints viaterminals with either a human or an AI natural language program, both hiddenfrom view. If the testers can’t reliably distinguish between the human and theprogram, intelligence is ascribed to the program.In 1991 Hugh Loebnerestablished theLoebner Prize, which would award $100,000 to the first AIprogram to pass the Turing Test.As of this writing, the Loebner Prize has notbeen awarded.

Dartmouth Workshop

The Dartmouth Workshop served

to bring researchers in this newly emergingfield together

to interact and to exchange ideas. Held during August of1956, theworkshopmarks the birth of artificial intelligence. AI seems alone amongdisciplines in having a birthday. Its parents included John McCarthy, MarvinMinsky, Herbert Simon and Allen Newell. Other eventually prominent attendeeswereClaude Shannon of Information Theory fame, Oliver Selfridge, thedeveloper of Pandemonium Theory, andNathaniel Rochester, a major designerof the very early IBM 701 computer.

John McCarthy, on the Dartmouth faculty at the time of the Workshop, iscredited with having coined the name Artificial Intelligence. He was also theinventor of LISP, the predominant AI programming language for a half century.McCarthy subsequently joined the MIT faculty and, later, moved to Stanfordwhere he established their AI Lab. As of this writing he’s still an active AI

researcher.

Marvin Minsky helped to found the MIT AI Lab where he remains an activeand influential AI researcher until the time of this writing.

Simon and Newell brought the only running AI program, the logical theorist,to the Dartmouth Workshop. It operated by means-ends analysis, an AI planningalgorithm. At each step it attempts to choose an operation (means) that movesthe system closer to its goal (end). Herbert Simon and Allen Newell founded theAI research lab at Carnegie Mellon University. Newell passed away in 1992, andSimon in 2001.i

Samuel’s Checker Player

Every computer scientist knows that a computer only executes an algorithm itwas programmed to run. Hence, it can only do what its programmer told it to do.Therefore it cannot know anything its programmer didn’t, nor do anything itsprogrammer couldn’t. This seemingly logical conclusion is, in fact, simply wrongbecause it ignores the possibility of a computer being programmed to learn. Suchmachine learning, later to become a major subfield of AI, began with ArthurSamuel’s checker playing program (1959). Though Samuel was initially able tobeat his program, after a few months of learning it’s said that he never wonanother game from it. Machine learning was born.

Minsky’s Dissertation

In 1951, Marvin Minsky and Dean Edmonds build the SNARC, the firstartificial neural network that simulated a rat running a maze. This work was thefoundation of Minsky’s Princeton dissertation (1954). Thus one of the foundersand major players in symbolic AIwas, initially, more interested in neural nets andset the stage for their computational implementation.

Perceptrons and the Neural Net Winter

Frank Rosenblatt’s perceptron (1958) was among the earliest artificial neuralnets. A two-layer neural net bestthought of as a binary classifier system, aperceptron maps its input vector into a weighted sum subject to a threshold,yielding a yes or no answer. The attraction of the perceptron was due to asupervised learning algorithm, by means of which a perceptron could be taughtto classify correctly. Thus neural nets contributed to machine learning.

Research on perceptrons came to an inglorious end with the publication ofthe Minsky and Pappert book (1969) in which they showed the perceptronincapable of learning to classify as true or false the inputs to such simplesystems as the exclusive or (XOR–

either A or B but not both). Minsky andPapert also conjectured that even mulit-layered perceptrons would prove to havesimilar limitations. Though this conjecture

proved to be mostly false, thegovernment agencies

funding AI research took it seriously. Funding for neuralnet research dried up, leading to a neural net winter that didn’t abate until thepublishing of the Parallel Distributing Processing volumes (McClelland andRumelhart 1986, Rumelhart and McClelland 1986).

The Genesis of Major Research Areas

Early in its history the emphasis of AI research was largely toward producingsystems that could reason about high-level, relatively abstract, but artificialproblems, problems that would require intelligence if attempted by a human.Among the first of such systems was Simon and Newell’s general problem solver(Newell, Shaw, Simon 1959),

which, like its predecessor the logical theorist,used means ends analysisto solve a variety of puzzles. Yet another earlyreasoning system was Gelernter’s geometry theorem prover,

Another important subfield of AI is natural language processing, concernedwith systems that understand. Among the first such was SHRDLU (Winograd1972), named after the order of keys on a linotype machine. SHRDLU couldunderstand and execute commands in English ordering it to manipulate woodenblocks, cones, spheres, etc. with a robot arm in what came to be known as ablocks world. SHRDLU was sufficiently sophisticated to be able to use theremembered context of a conversation to disambiguate references.

It wasn’t long, however, before AI researchers realized that reasoning wasn’tall there was to intelligence. In attempting to scale their systems up to deal withreal world problems, they ran squarely into the wall of the lack of knowledge.Real world problems demanded that the solver know something. So, knowledgebased systems, often called expert systems, were born. The name came fromthe process of knowledge engineering, of having knowledge engineerslaboriously extract information from human experts, and handcraft thatknowledge into their expert systems.

Lead by chemistJoshua Lederberg, and AI researchers

Edward Feigenbaum

andBruce Buchanan, the first such expert system, called Dendral was an expertin organic chemistry. DENDRAL helped to identify the molecular structure oforganic molecules by analyzing data from a mass spectrometer and employingits knowledge of chemistry(Lindsay, Buchanan, Feigenbaum, and Lederberg.1980). The designers of DENDRAL added knowledge to its underlying reasoningmechanism, an inference engine, to produce an expert system capable ofdealing with a complex, real world problem.

A second such expert system, called Mycin (Davis, Buchanan and Shortliffe.1977), helped physicians diagnose and treat infectious blood diseases andmeningitis. Like DENDRAL, Mycin relied on both hand crafted expert knowledgeand a rule based inference engine. The system was successful in that it coulddiagnose difficult cases as well as the most expert physicians, but unsuccessfulin that it was never fielded. Inputting information into Mycin required about twentyminutes. A physician would spend at most five minutes on such a diagnosis.

Research During the Neural Net Winter

Beginning with the publication ofPerceptrons

(Minsky and Papert 1969), theneural net winter lasted almost twenty years. The book had mistakenly convincedgovernment funding agencies that the neural net approach was unpromising. Inspite of this appalling lack of funding, significant research continued to beperformed around the world. Intrepid researchers who somehow managed tokeep this important research going included Amari andFukushima

in Japan,Grossberg and Hopfield in the United States, Kohonen in Finland, and von derMalsberg in Germany. Much of this work concerned self-organization of neuralnets, and learning therein. Much was also motivated by the backgrounds of theseresearchers in neuroscience.

The Rise of Connectionism

The end of the neural net winter was precipitated by the publication of thetwo Parallel Distributed Processing volumes (Rumelhart and McClelland 1986,McClelland and Rumelhart 1986). They were two massive, edited volumes withchapters authored by members of

the PDP research group, then at the Universityof California, San Diego. These volumes gave rise to the application of artificialneural nets, soon to be called connectionism, to cognitive science. Whetherconnectionism was up to the job of explaining mind, rapidly became a hot topic ofdebate

among philosophers, psychologists and AI researchers (Fodor andPylyshyn 1988, Smolensky 1987,Chalmers1990). The debate has died downwith no declared winner, and with artificial neural nets becoming an establishedplayer in the current AI field.

In addition to its success in the guise of connectionism for cognitivemodeling, artificial neural nets have found a host of practical applications. Most ofthese involve pattern recognition. They include mutual fund investing, frauddetection, credit scoring,real estate appraisal, and a host of others. This wideapplicability has been primarily the result of a widely used training algorithmcalled back propagation. Though subsequently traced to much earlier work, backpropagation was rediscovered by the PDP research group, and constituted thepreeminent tool for the research reported in the two PDP volumes.

The AI Winter

Due to what turned out to be an overstatement of the potential and timing ofartificial intelligence, symbolic AI suffered its own winter. Asan example, in 1965Herbert Simon predicted“machines will be capable, within twenty years, of doingany work that a man can do.” This and other such predictions did not come topass. As a result, by the mid-nineteen-eighties government agency funding forAIbegan to dry up and commercial investment became almost non-existent.Artificial intelligence became a taboo word in the computing industry for a decadeor more, in spite of the enormous success of expert systems (more below). TheAI spring didn’t arrive until the advent of the next “killer” application, video games(again more below).

Soft computing

The term “soft computing” refers to a motley assemblage of computationaltechniques designed to deal with imprecision, uncertainty, approximation, partialtruths, etc. Its methods tend to be inductive rather than deductive. In addition toneural nets, which we’ve already discussed, soft computing includes evolutionarycomputation, fuzzy logic, and Bayesian networks. We’ll describe each in turn.

Evolutionary computation began with a computational rendition of naturalselection called genetic algorithms (Holland 1975). A population searchalgorithm, it typically begins with a population of artificial genotypes representingpossible solutions to the problem at hand. The members of this population aresubjected to mutation (random changes) and crossover (the intermixing of twogenotypes). The resulting new genotypes are input to a fitness function thatmeasures the quality of the genotype. The most successful of these genotypesconstitute the next population, and the process repeats. If well designed, thegenotypes in the population tend over time to become much alike, thusconverging to a desired solution and completing the genetic algorithm. Inaddition, evolutionary computation also includes classifier systems, whichcombine rule-based and reinforcement ideas with genetic algorithms.Evolutionary computation also includes genetic programming, a method of usinggenetic algorithms to search for computer programs, typically in LISP, that willsolve a given problem.

Derived from Zadeh’s fuzzy set theory, in which degrees of set membershipbetween 0 and 1 are assigned (1965), fuzzy logic has become a mainstay of softcomputing. Using if then rules with fuzzy variables,fuzzy logic has beenemployed in a host of control applications including home appliances, elevators,automobile windows, cameras and video games. References are not given sincethese commercial applications are almost always proprietary.

A Bayesian network, with nodes representing situations, uses Bayes’theorem on conditional probability to associate a probability with each of its links.Such Bayesian networks have been widely used for cognitive modeling, generegulation networks, decision support systems, etc. They are an integral part ofsoft computing.

Recent Major Accomplishments

We’ll conclude our brief history of AI with an account of some of its relativelyrecent major accomplishments. These include expert systems, chess players,theorem provers, and a new killer application. Each will be described in turn.

Knowledge based expert systems

Though knowledge based expert systems made their appearance relativelyearly in AI history, they became a major, economically significant, AI applicationsomewhat later. Perhaps the earliest such commercially successful expertsystem was R1, later renamed XCON (McDermott 1980). XCON saved millionsfor DEC (Digitial Equipment Corporation) by effectively configuring their VAXcomputers before delivery, rather than having DEC engineers solve problemsafter their delivery. Other such applications followed, including diagnostic andmaintenance systems for Campbell Soups’ cookers and GE locomotives. A FordMotor Company advertisement for a piece of production machinery stipulatedthat such a diagnostic and maintenance expert system be a part of everyproposal. One book detailed 2500 fielded expert systems. Expert systemsconstituted the first AI killer application. It was not to be the last.

Deep Blue beating Kasparov

Early AI researchers tended to work on problems that would requireintelligence if attempted by a human. One such problem was playing chess. AIchess players appeared not long after Samuel’s checker player. Among the mostaccomplished of these chess playing systems was IBM’s Deep Blue, which in1997 succeeded in defeating world champion Gary Kasparov in a six-gamematch, belatedly fulfilling another of Herbert Simon’s early predictions. Thoughrunning on a specially built computer and provided with much chess knowledge,Deep Blue depended ultimately upon traditional AI game-playing algorithms. Thematch with Kasparov constituted an AI triumph.

Solution of the Robbins conjecture

Another, even greater, AI triumph was soon to follow. In a 1933 paper E.V.Huntington gave a new set of three axioms that characterized a Boolean algebra,a formal mathematical system important to theoretical computer science. Thethird of these axioms was so complex as to be essentially unusable. Thusmotivated, Herbert Robbins soon replaced

this third axiom with a simpler one,and conjectured that this new three-axiom set also characterized Booleanalgebras. This Robbins conjecture remained one of a host of such in themathematical literature until the prominent logician and mathematician AlfredTarski called attention to it, turning it into a famous unsolved problem. Afterresisting the efforts of human mathematicians for over half a century, theRobbins conjecture finally succumbed to the banishments of ageneral purpose

Employing more AI practitioners than any other, the computer and videogame industry is enjoying a screaming success. According to one reliablesource, the Entertainment Software Association, 2004 sales topped seven billiondollars, with almost 250 million such games sold. AI’s role in this astoundingsuccess is critical; its use is essential to producing the needed intelligentbehavior on the part of the virtual characters who populate the games. Wikipediahas an entry entitled “game artificial intelligence” that includes a history of theever increasing sophistication of AI techniques used in such

games, as well asreferences to a half-dozen or so books on applying AI to games. At this writingthere seems to be an unbounded demand for AI workers in the game industry.This highly successful commercial application is yet another triumph for AI.

Major

AI Research Areas

There are almost a dozen distinct subfields of AI research each with its ownspecialized journals, conferences, workshops, etc. This section will provide aconcise account of the research interests in each of these subfields.

Knowledge Representation

Every AI system, be it a classical AI system with humans providing input andusing the output, or an autonomous agent (Franklin and Graesser 1997), mustsomehow translate input (stimuli) into information or knowledge to be used toselect output (action). This information or knowledge must somehow berepresented within the system so that it can be processed to help determineoutput or action. The problems raised by such representation constitute thesubject matter of research in the AI subfield

commonly referred to as knowledgerepresentation.

In AI systems, one encounters knowledge represented using such logicalformalisms such as propositional logic and first-order predicate calculus. Onemay also find network representations such as semanticnets whose nodes andlinks have labels providing semantic content. The underlying idea is that aconcept, represented by a node, gains meaning via it relationships (links) to otherconcepts. More complex data structures such as production rules, frames, andfuzzy sets are also used. Each of these data structures has its own type ofreasoning or decision-making apparatus, its inference engine.

The issue of to represent or not seems to have been implicitly settled, as thearguments have died down. Rodney Brooks of the MIT AI Lab seems to havemade his point that more than was previously thought could be accomplishedwithout representation (1991). His opponents, however, have carried the day, inthat representations continue to be widely used. I believe

that representationsare critical for the process of deciding what action to take, and much less so forthe process of executing the action. This seems to be the essence of the issue.

Heuristic Search

Search problems such as the traveling salesman problem have been studiedin computer science almost since its inception. For example, find the mostefficient route for a salesman to take to visit each of N cities exactly once. Allknown algorithms for finding optimal solutions to such a problem increaseexponentially

with N, meaning that for large numbers of cities no optimal solutioncan be found. However, good enough solutions can be found using heuristicsearch algorithms from AI. Such algorithms employ knowledge of the particulardomain in the form of heuristics,rules of thumb, that are not guaranteed to findthe best solution, but that most often find a good enough solution.

Such heuristic search algorithms are widely used for scheduling, for datamining (finding patterns in data), for constraint satisfaction problems, for games,for searching the web, and for many other such applications.

Planning

An AI planner is a system that automatically devises a sequence of actionsleading from an initial real world state to a desired goal state. Planners may beused, for example, to schedule work on a shop floor, to find routes for packagedelivery, or to assign usage of the Hubble telescope. Research on such planningprograms is a major subfield of AI. Fielded applications are involved in spaceexploration, military logistics, and plant operations and control.

Expert Systems

Knowledge based expert systems were discussed in the previous sections.As a subfield of AI expert systems researchers are concerned with reasoning(improving inference engines for their systems), knowledge representation (howto represent needed facts to their systems) and knowledge engineering (how toelicit knowledge from experts that’s sometimes implicit. As we’ve seen above,their fielded applications are legion.

Machine Vision

Machine or computer

vision is a subfield of AI devoted to the automatedunderstanding of visual images, typically digital photographs. Among its manyapplications are product inspection, traffic surveillance and military intelligenceii.With images multiplying every few seconds from satellites, high-flying spy planesand autonomous drones, there aren’t enough humans to interpret and index theobjects in the images so that they can be understood and located. Researchtoward automating this process is just starting. AI researchin machine vision isalso beginning to be applied to security video cameras so as to understandscenes and alert humans when necessary.

Machine Learning

The AI subfield of machine learningiii

is concerned with algorithms that allowAI systems to learn (seeSamuel’s checker player above). Though machinelearning is as old as AI itself, its importance has increased as more and more AIsystems, especially autonomous agents (see below), are operating inprogressively more

complex and dynamically changing domains. Much ofmachine learning is supervised learning in which the system isinstructed

usingtraining data. Unsupervised, or self-organizing systems, as mentioned above, arebecoming common. Reinforcement learning, accomplished with artificial rewards,is typical for learning new tasks. There is even a new subfield of machinelearning devoted to developmental robotics, robots that go through a rapid earlylearning phase, as do human children.

Natural Language Processing

The AI subfield of natural language processing includes both the generationand the understanding of natural language, usually text. It’s history dates back tothe Turing test (see above). Today it’s a flourishing field of research into machinetranslation, question answering, automatic summarization, speech recognitionand other areas. Machine translators, though typically only 90% or so accurate,can increase the productivity of human translators fourfold. Text recognitionsystems are being developed for the automatic input of medical histories. Voicerecognition enables spoken commands to a computer and even dictation.

Software agents

An autonomous agent is defined to be a system situated in an environment,and a part of that environment, that senses the environment and acts on it, overtime, in pursuit of its own agenda, in such a way that its actions can influencewhat it later senses (Franklin and Graesser 1997). Artificial autonomous agentsinclude software agents and some robots. Autonomous software agents come inseveral varieties. Some like the author’s IDA “live” in an environment includingdatabases and the internet, and autonomously perform a specified task such asassigning new jobs for sailors at the end of a tour of duty. Others, sometimescalled avatars, have virtual faces or bodies displaying on monitors that allowsthem to interact more naturally with humans, often providing information. Stillothers, called conversational virtual agents, simulate humans, and interactconversationally with them in chat rooms, some so realistically

as to be mistakenfor humaniv. Finally, there are virtual agents as characters in computer and videogames.

Intelligent Tutoring Systems

Intelligent tutoring systems are AI systems, typically software agents, whosetask it is to tutor students interactively one on one, much as a human tutor would.Results from early efforts in this direction were disappointing. Later systems weremore successful in domains such as mathematics that lend themselves to shortanswers from the student.More recently intelligent

tutoring systems likeAutoTutor have been developed that can deal appropriately with full paragraphswritten by the student. Today the major bottleneck in this research is gettingdomain knowledge into the tutoring systems. As a result, research in variousauthoring tools has flourished.

Robotics

In its early days robotics was a subfield of mechanical engineering with mostresearch being devoted to developing robots capable of executing particularactions, such as grasping, walking, etc. Their control systems were purelyalgorithmic, with no AI components. As robots became more capable, the needfor more intelligent control structures became apparent, and cognitive roboticsresearch involving AI-based control structures was born. Today, robotics and AIresearch have a significant and important overlap (more below).

Recent Trends

As 2007 began, artificial intelligence has not only emerged from its AI winterinto an AI spring, but that spring has morphed into a full-fledged AI summer withits luxuriant growth

of fruit. Flourishing recent trends include soft computing,agent based AI, cognitive computing, developmental robotics, and artificialgeneral intelligence. Let’s look at each of these in turn.

Soft computing

In addition to the components described earlier, namely neural nets,evolutionary computing and fuzzy logic, soft computing is expanding into hybridsystems merging symbolic and connectionist AI. Prime examples of such hybridsystems are ACT-R, CLARION, and the author’s LIDA. Most such hybridsystems, including the three examples, were intended as cognitive models.Some of them underlie the computational architectures of practical AI programs.Soft computing now also includes artificial immune systems with their significantcontributions to computer security as well as applications to optimization and toprotein structure prediction.

AI for data mining

Along with statistics, AI provides indispensable tools for data mining, theprocess of searching large databases for useful patterns of data. Many of thesetools have been derived from research in machine learning. As databases rapidlyincrease in content, data mining become more and more useful, leading to atrend toward researching AI tools for data mining.

Agent based AI

The situated, or embodied, cognition movement (Varela Thompson andRosch 1991), in the form of agent based AI, has clearly carried the day in AIresearch. Today, most newly fielded AI systems are autonomous agents of somesort. The dominant AI textbook (Russell and Norvig 2002), usedin over 1000universities world wide, is theleading text

partially because its first edition wasthe first agent based AI textbook. Applications of AI agents abound. Some werementioned in the section on software agents above.

Cognitive computing

Perhaps

the newest, and certainly among the most insistent, current trendsin AI research is what has come to be called cognitive computingv. Cognitivecomputing includes cognitive robotics, development robotics, self-awarecomputing systems, autonomic computingsystems and artificial generalintelligence. We’ll briefly describe each in turn.

As mentioned above, robotics in its early days was primarily concerned withhow

to perform actions, and was mostly a mechanical engineering discipline.More recently this emphasis is shifting to action selection, that is, to decidingwhat

action to perform.Cognitive robotics, the endowing of robots with morecognitive capabilities, was born, and is becoming an active subfield of AI.

Another closely related new AI research discipline,developmental robotics,combines robotics, machine learning and developmental psychology. The idea isenable robots to learn continually as humans do. Such learning should allowcognitive robots to operate in environments too complex and too dynamic for allcontingencies to be hand crafted into the robot. This new discipline is supportedby the IEEE Technical Committee on Autonomous Mental Development.

Government agencies are investing in cognitive computing in the form ofself-aware computing systems. DARPA, the Defense Advanced Research ProgramsAgency sponsored the Workshop on Self-aware Computer Systems. RonBrachman, then director of the DARPA IPTO program office, and since thepresident of AAAI, the Association for the Advancement of Artificial Intelligence,spelled it out thusly:

“A truly cognitive system would be able to ... explain what it was doing andwhy it was doing it. It would be reflective enough to know when it washeading down a blind alley or when it needed to ask for information

that itsimply couldn't get to by further reasoning. And using these capabilities, acognitive system would be robust in the face of surprises. It would be ableto cope much more maturely with unanticipated circumstances than anycurrent machine can.”

DARPA is currently supporting research on such biologically inspired cognitivesystems.

IBM Research is offering commercially oriented support for cognitivecomputing through what it refers to asautonomic computing. The primary interesthere is in self-configuring, self-diagnosing and self-healing systems.

A very recent and not yet fully developed trend in AI research is the movetoward systems exhibiting a more human-like general intelligence, beginning tobe calledartificial general intelligence

(AGI). The development of this AGI trendcan be traced through a sequence of special tracks, special sessions, symposiaand workshops:

Such AGI systems being developed include LIDA,Joshua Blue, andNovamente.

AI and Cognitive Science

The science side of AI is devoted primarily to modeling human cognition. Itsapplication is to provide hopefully testable hypotheses for cognitive scientists andcognitive neuroscientists. In addition to cognitive models with more limitedtheoretical ambition, integrated models of large portions of cognition have beendeveloped. These include SOAR, ACT-R, CLARION, and LIDA. Some of themhave been implemented computationally as software agents, becoming part ofembodied cognition. One of them, LIDA, implements several differentpsychological theories, including global workspace theory, working memory,perception by affordances and transient episodic memory. The importance of thiscognitive modeling subfield of AI has been

recognized by a few computerscience department offering degree programs in cognitive science.

The core themes—

where do they stand now?

Smart Software vs. Cognitive Modeling

As throughout AI history, both pursuits are still active in AI research, theengineering side and the science side. Currently, both are moving toward a moregeneral approach. Smart software is beginning to include AGI. Cognitivemodeling is moving toward more integrated hybrid models such as ACT-R,CLARION and LIDA, in addition to its traditional interest in more specializedmodels. Another major push on the smart software side is toward moreautonomous software agent systems.

Symbolic AI vs. Neural Nets

Both symbolic AI and neural nets have survived their respective winters and arenow flourishing. Neither

side of the controversy has won out. Both continue to bequite useful. They are even coming together in such hybrid systems as ACT-R,CLARION and LIDA. ACT-R melds symbolic and neural net features. CLARIONconsists of a neural net module interconnected with a symbolic module. LIDAincorporates passing activation throughout an otherwise symbolic system makingit also quite neural-net like.

Reasoning vs. Perception

Research into AI reasoning continues unabated in such subfields as search,planning and expert systems. Fielded practical applications are legion.Perception has come into its own in machine vision, agent based computing andcognitive robotics. Note that they come together in the last two, as well as inintegrated cognitive modeling and AGI.

Reasoning vs. Knowledge

In addition to reasoning, knowledge plays a critical role in expert systems, and inagent based computing, self-aware computing and autonomic computing also.Again both are alive and flourishing, with the importance of adding knowledge

topractical system ever more apparent. Data-mining has become another way ofacquiring such knowledge..

To Represent or Not

Without representation, Brooks’ subsumption architecture accords each layer itsown senses and ability to choose and perform itssingle act. A higher level can,when appropriate, subsume the action of the next lower level. With thissubsumption architecture controlling robots, Brooks successfully made his pointthat much could and should be done with little or no representation. Still,representation is almost ubiquitous in AI systems as they become able to moreintelligently deal with ever more complex, dynamic environments. It would seemthat representation is critical to the process of action selection in AI systems, butmuch less

so to the execution of these actions. The argument over whether torepresent seems to have simply died away.

Brain in a Vat vs. Embodied AI

For once we seem to have a winner. Embodied, or situated, AI has simply takenover, as most of the new research into AI systems is agent based. Perusal of thetitles of talks at any of the general AI conferences like AAAI or IJCAI makes thisabundantly clear.

Narrow AI vs. Human Level Intelligence

Narrow AI continues to flourish unabated, while the pursuit of human levelintelligence in machines is gaining momentum via AGI.

Except for the strong move of AI research toward embodiment, each side ofevery issue continues to be strongly represented in today’s AI research.Research into artificial intelligence is thriving

as never before, and promisescontinuing contributions, both practical to engineering and theoretical to science.